Study rechecks where Platonic representation alignment appears in newer models
Original title: Where Does Alignment Emerge and What Does It Mean? A Closer Look at the Platonic Representation Hypothesis
The capability-alignment trend holds when captions are clean, but same-modality alignment still far exceeds cross-modal scores.
A paper examines where alignment between unimodal models appears across layers and representation dimensions under the Platonic Representation Hypothesis. It finds the capability-alignment trend continues for newer language models when captions are sufficiently clean and informative. Cross-modal alignment of unimodal models can exceed that of explicitly aligned vision-language models, yet remains well behind same-modality alignment. Code is released.
- The work tests alignment across model layers and representation dimensions.
- Newer-model alignment persists only with clean, informative captions.
- Unimodal cross-modal scores can beat explicitly aligned vision-language models.
- Same-modality alignment remains substantially higher than cross-modal alignment.
- Code is at github.com/ExplainableML/prh-closer-look.
From the original article
1 min read at arxiv.orgThe Platonic Representation Hypothesis (PRH) suggests that neural networks are converging toward similar representation spaces. As language models become more capable, their representations are aligning with those of vision models. However, recent work has questioned whether this trend continues to hold for newer language models.
By Julia Herbst · arxiv.org
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